{
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    },
    "colab": {
      "name": "titanic-beginner-s-guide-with-sklearn.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "uNEaYNcX6dw7",
        "grfXEA6a6dxW"
      ]
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "08c5693b1c47e47bb5ddd0979f3865b9b0de95c9",
        "id": "M32pOWMJ6dwA",
        "colab_type": "text"
      },
      "source": [
        "# Machine Learning to Predict Titanic Survivors \n",
        "Hi, I'm a current undergraduate student interested in the Data Science and Machine Learning field. In this Kernel, I will try to step by step build a ML model using sklearn to predict the outcomes of each passenger aboard the titanic. This guide is meant for people starting with data visualization, analysis and Machine Learning. If that sounds like you, then you're in the right place! It is not as difficult as you think to understand.\n",
        "\n",
        "*Please upvote and share if this helps you!! Also, feel free to fork this kernel to play around with the code and test it for yourself. If you plan to use any part of this code, please reference this kernel!* I will be glad to answer any questions you may have in the comments. Thank You! \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "7e51a8d8c11aeb90bb74e1519b537203318a6ca8",
        "id": "dADrFgPu6dwD",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "b5a9e650ed4ee4bcb942881e39476de033684e8f",
        "id": "Cn4JKuAq6dwE",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"p1\"></a>\n",
        "# 1. Importing Libraries and Packages\n",
        "We will use these packages to help us manipulate the data and visualize the features/labels as well as measure how well our model performed. Numpy and Pandas are helpful for manipulating the dataframe and its columns and cells. We will use matplotlib along with Seaborn to visualize our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "id": "0NcvPNm16dwF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "sns.set_style(\"whitegrid\")\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "9b574000bd7d01fc11bd1a2a129512c420ae9d60",
        "id": "Cu9hJxYs6dwH",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"p2\"></a>\n",
        "# 2. Loading and Viewing Data Set\n",
        "With Pandas, we can load both the training and testing set that we wil later use to train and test our model. Before we begin, we should take a look at our data table to see the values that we'll be working with. We can use the head and describe function to look at some sample data and statistics. We can also look at its keys and column names."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "scrolled": true,
        "id": "c_9i2phM6dwI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "training = pd.read_csv(\"http://iali.in/datasets/titanic/train.csv\")\n",
        "testing = pd.read_csv(\"http://iali.in/datasets/titanic/test.csv\")\n",
        "\n",
        "#data-source: https://www.kaggle.com/c/titanic/data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "17d595b96b41eecfaeb9185bca85c7b5abd8d2a1",
        "id": "S82Gr5F96dwJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "8c303a8d-412c-4b6d-cdcc-5a4d2f390698"
      },
      "source": [
        "training.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "ea5cc5601d9989c520b8269f0d3434fd4b7c4128",
        "id": "lVX4qadu6dwM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "92a90b18-b7c3-474e-f178-f1658a9ae126"
      },
      "source": [
        "training.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
              "mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
              "std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
              "50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
              "75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
              "max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "3d25ce4c089559faf5ab3b78667e6201a62e57da",
        "id": "mlnVGVlQ6dwN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "9b986ed3-7ee5-4312-c9d4-1572a91c7014"
      },
      "source": [
        "print(training.keys())\n",
        "print(testing.keys())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
            "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
            "      dtype='object')\n",
            "Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
            "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "e6d7efeb33bee9b004b0a309c21feb9c90bde389",
        "id": "aVpTfbpe6dwP",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"p3\"></a>\n",
        "# 3. Dealing with NaN Values (Imputation)\n",
        "There are NaN values in our data set in the age column. Furthermore, the Cabin column has too many missing values and isn't useful to be used in predicting survival. We can just drop the column as well as the NaN values which will get in the way of training our model. We also need to fill in the NaN values with replacement values in order for the model to have a complete prediction for every row in the data set. This process is known as **imputation** and we will show how to replace the missing data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "fc61dad51cca67aafca7447fb02b58d8bbb2a226",
        "scrolled": true,
        "id": "uBwePb436dwQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "fb293286-1ce5-4baa-e0ee-118a07f3a323"
      },
      "source": [
        "def null_table(training, testing):\n",
        "    print(\"Training Data Frame\")\n",
        "    print(pd.isnull(training).sum()) \n",
        "    print(\" \")\n",
        "    print(\"Testing Data Frame\")\n",
        "    print(pd.isnull(testing).sum())\n",
        "\n",
        "null_table(training, testing)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data Frame\n",
            "PassengerId      0\n",
            "Survived         0\n",
            "Pclass           0\n",
            "Name             0\n",
            "Sex              0\n",
            "Age            177\n",
            "SibSp            0\n",
            "Parch            0\n",
            "Ticket           0\n",
            "Fare             0\n",
            "Cabin          687\n",
            "Embarked         2\n",
            "dtype: int64\n",
            " \n",
            "Testing Data Frame\n",
            "PassengerId      0\n",
            "Pclass           0\n",
            "Name             0\n",
            "Sex              0\n",
            "Age             86\n",
            "SibSp            0\n",
            "Parch            0\n",
            "Ticket           0\n",
            "Fare             1\n",
            "Cabin          327\n",
            "Embarked         0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "4dfb986a769ce38cb258a3e8f0fc2b4c9490612f",
        "id": "8RL6Mln06dwR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "34891a30-4c59-4325-fe12-9dd792421518"
      },
      "source": [
        "training.drop(labels = [\"Cabin\", \"Ticket\"], axis = 1, inplace = True)\n",
        "testing.drop(labels = [\"Cabin\", \"Ticket\"], axis = 1, inplace = True)\n",
        "\n",
        "null_table(training, testing)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data Frame\n",
            "PassengerId      0\n",
            "Survived         0\n",
            "Pclass           0\n",
            "Name             0\n",
            "Sex              0\n",
            "Age            177\n",
            "SibSp            0\n",
            "Parch            0\n",
            "Fare             0\n",
            "Embarked         2\n",
            "dtype: int64\n",
            " \n",
            "Testing Data Frame\n",
            "PassengerId     0\n",
            "Pclass          0\n",
            "Name            0\n",
            "Sex             0\n",
            "Age            86\n",
            "SibSp           0\n",
            "Parch           0\n",
            "Fare            1\n",
            "Embarked        0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "b8cd1cb47f574b46b2058f2e3635d034ce9a57e5",
        "id": "cP9-Qi-J6dwT",
        "colab_type": "text"
      },
      "source": [
        "We take a look at the distribution of the Age column to see if it's skewed or symmetrical. This will help us determine what value to replace the NaN values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "b791d174a23371a6a9748906771189bf45051ff2",
        "id": "DnlX0JJI6dwT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "317786bc-8918-4d1c-b412-876f4b116427"
      },
      "source": [
        "copy = training.copy()\n",
        "copy.dropna(inplace = True)\n",
        "sns.distplot(copy[\"Age\"])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f950830e3c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j9ClN7VZa+tAtEKZVExGs8WBEvRVXdieAjGHyhnWlfp4zng++OMvaV4t54wf/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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "c04adcb431b42cb574ee0741fdb86925b83863ff",
        "id": "4ccTVxle6dwV",
        "colab_type": "text"
      },
      "source": [
        "* Looks like the distribution of ages is slightly skewed right. Because of this, we can fill in the null values with the median for the most accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "dfa607948743e80892fdf82a000c7a96873d38ce",
        "id": "0_YumH9G6dwW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "001f124f-80bb-4a3e-eda9-a5812512ad5a"
      },
      "source": [
        "#the median will be an acceptable value to place in the NaN cells\n",
        "training[\"Age\"].fillna(training[\"Age\"].median(), inplace = True)\n",
        "testing[\"Age\"].fillna(testing[\"Age\"].median(), inplace = True) \n",
        "training[\"Embarked\"].fillna(\"S\", inplace = True)\n",
        "testing[\"Fare\"].fillna(testing[\"Fare\"].median(), inplace = True)\n",
        "\n",
        "null_table(training, testing)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data Frame\n",
            "PassengerId    0\n",
            "Survived       0\n",
            "Pclass         0\n",
            "Name           0\n",
            "Sex            0\n",
            "Age            0\n",
            "SibSp          0\n",
            "Parch          0\n",
            "Fare           0\n",
            "Embarked       0\n",
            "dtype: int64\n",
            " \n",
            "Testing Data Frame\n",
            "PassengerId    0\n",
            "Pclass         0\n",
            "Name           0\n",
            "Sex            0\n",
            "Age            0\n",
            "SibSp          0\n",
            "Parch          0\n",
            "Fare           0\n",
            "Embarked       0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "8ae1ecf061805ccaf6663b50bf4c21252f4195a4",
        "id": "TjBnLEDL6dwX",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"p4\"></a>\n",
        "# 4. Plotting and Visualizing Data\n",
        "It is very important to understand and visualize any data we are going to use in a machine learning model. By visualizing, we can see the trends and general associations of variables like Sex and Age with survival rate. We can make several different graphs for each feature we want to work with to see the entropy and information gain of the feature. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "14ac153fd1d8992bed821c28532da3dd99a3ce2d",
        "id": "7P54B-Na6dwY",
        "colab_type": "text"
      },
      "source": [
        "**Gender**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "a313f660ca8133f5c625814a6c599f5c527a9b22",
        "id": "xaCKUzC86dwY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "b3aaad06-5237-4315-839f-2b7b9a0295f3"
      },
      "source": [
        "#can ignore the testing set for now\n",
        "sns.barplot(x=\"Sex\", y=\"Survived\", data=training)\n",
        "plt.title(\"Distribution of Survival based on Gender\")\n",
        "plt.show()\n",
        "\n",
        "total_survived_females = training[training.Sex == \"female\"][\"Survived\"].sum()\n",
        "total_survived_males = training[training.Sex == \"male\"][\"Survived\"].sum()\n",
        "\n",
        "print(\"Total people survived is: \" + str((total_survived_females + total_survived_males)))\n",
        "print(\"Proportion of Females who survived:\") \n",
        "print(total_survived_females/(total_survived_females + total_survived_males))\n",
        "print(\"Proportion of Males who survived:\")\n",
        "print(total_survived_males/(total_survived_females + total_survived_males))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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z5s1DYGAgoqOjUVhYaFP///mf/4mhQ4ciJiYGsbGxDQ5ML1u2DLW1tYiKisLAgQPx0ksv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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Total people survived is: 342\n",
            "Proportion of Females who survived:\n",
            "0.6812865497076024\n",
            "Proportion of Males who survived:\n",
            "0.31871345029239767\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "e924d85d07f0da76ad085c5ca8f26172a80ad987",
        "id": "B8j8RO_z6dwa",
        "colab_type": "text"
      },
      "source": [
        "> Note that the numbers printed above are the proportion of male/female survivors of all the surviviors ONLY. The graph shows the propotion of male/females out of ALL the passengers including those that didn't survive."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "69f48dadbaa468414c542c74af8bfe98a68ec20d",
        "id": "AVD2f4UU6dwa",
        "colab_type": "text"
      },
      "source": [
        "Gender appears to be a very good feature to use to predict survival, as shown by the large difference in propotion survived. Let's take a look at how class plays a role in survival as well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "8479cb62de593c2945b112b645a527be7aa776f3",
        "id": "h5YB5bhD6dwb",
        "colab_type": "text"
      },
      "source": [
        "**Class**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d55d78e57c418f2ea9f57c6ae649dc9f044ef553",
        "id": "Ra4_UXFj6dwb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "f3e103fd-32ec-45ac-f57b-281daed50623"
      },
      "source": [
        "sns.barplot(x=\"Pclass\", y=\"Survived\", data=training)\n",
        "plt.ylabel(\"Survival Rate\")\n",
        "plt.title(\"Distribution of Survival Based on Class\")\n",
        "plt.show()\n",
        "\n",
        "total_survived_one = training[training.Pclass == 1][\"Survived\"].sum()\n",
        "total_survived_two = training[training.Pclass == 2][\"Survived\"].sum()\n",
        "total_survived_three = training[training.Pclass == 3][\"Survived\"].sum()\n",
        "total_survived_class = total_survived_one + total_survived_two + total_survived_three\n",
        "\n",
        "print(\"Total people survived is: \" + str(total_survived_class))\n",
        "print(\"Proportion of Class 1 Passengers who survived:\") \n",
        "print(total_survived_one/total_survived_class)\n",
        "print(\"Proportion of Class 2 Passengers who survived:\")\n",
        "print(total_survived_two/total_survived_class)\n",
        "print(\"Proportion of Class 3 Passengers who survived:\")\n",
        "print(total_survived_three/total_survived_class)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TTz7BlStXMGTIEMycOROzZs2q9xqX+ixcuNA4nNWvXz+MGDEC+/fvx/Dhw2u1nTJlCjQa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\nYQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Total people survived is: 342\n",
            "Proportion of Class 1 Passengers who survived:\n",
            "0.39766081871345027\n",
            "Proportion of Class 2 Passengers who survived:\n",
            "0.2543859649122807\n",
            "Proportion of Class 3 Passengers who survived:\n",
            "0.347953216374269\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "205515b33d6dd0962cab4fb825caa45c77e5ec12",
        "id": "PMJupoSn6dwd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "037a353e-b2ff-42cc-c2c5-1ecf957886a6"
      },
      "source": [
        "sns.barplot(x=\"Pclass\", y=\"Survived\", hue=\"Sex\", data=training)\n",
        "plt.ylabel(\"Survival Rate\")\n",
        "plt.title(\"Survival Rates Based on Gender and Class\")\n",
        "#help(sns.barplot)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Survival Rates Based on Gender and Class')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g5OQER0dHAEBwcDCOHTumFgT79u3DyJEj0bRpUwCAnZ3di64HERG9II1BEBER\nAQDo0aMHjh07pnXDBQUFaNmypWpaKpVW6lLKzs4GAAwfPhwVFRWIiorC22+/rfUyiIjo5dUYBEql\nEg8fPoStrS0AoKysDAcPHsTWrVtx5MiRl164UqnEjRs38NVXXyE/Px+jRo1CUlISmjRpUu1r5HI5\nMjMzX3rZ9VWnTp0MXcILeZW3iba47aihqjYIUlJSEB8fD0tLS7Rq1QqTJ0/G7Nmz0aVLF60GnZNK\npcjPz1dNFxQUQCqVVprH1dUVpqamcHR0RKtWrZCdnV1p2OvnmZubN9hfuFcZt0nDxW0nDjUFfrVB\nsGHDBiQmJsLJyQmXL1/GsGHDsGbNGvj5+Wm10C5duiA7Oxu5ubmQSqVISUnBihUr1Obp168fUlJS\nEB4ejsLCQmRnZ6uOKRARkX5UGwSmpqZwcnICAHTu3BmtWrXSOgSAp2cbxcfHY/z48VAqlQgPD0f7\n9u2xevVquLi4oG/fvujTpw9+/PFHBAUFwdjYGDNmzECzZs1efq2IiOqBtLQ07Nu3D5GRkfDy8jJ0\nOdWqNgju37+PrVu3qqYfPXqkNj1u3DiNjfv6+sLX11ftsedHLpVIJJg1axZmzZpVq6KJiBqCbdu2\nISsrCzKZrGEGQWRkJEpKSqqdJiKimslkMrWf9VW1QRAVFaXPOoiIyEA0DjFBRESvNgYBEZHIMQiI\niESu2mMEz58hVBVtzhoiIqL6r9og4BlCRETiwLOGiIhETuPoo3K5HPv37+etKomIXlG8VSURkcjp\n9FaVRERU/2kMgr/eqrK4uJi3lCQieoVofavKmJgY1a0qnx84joiIGjaNQRAWFgZjY2P07NmzVreq\nJCKihkFj11Dfvn0xZ84cnD17FoIg6KMmIiLSI41BcOTIEfTq1Qs7d+6En58fEhISkJ6ero/aiIj0\nRiiXa56pnqmrmjV2DVlaWiIoKAhBQUF4+PAhFi5ciNGjR/OG10T0SpGYmCMnoUudtlleaAvABOWF\nN+q8bQB4M/6XOmlHYxAAwPnz53H48GGcPn0aLi4u+OKLL+pk4UREZHgag8DPzw+dOnVCYGAgZsyY\ngUaNGumjLiIi0hONQfCf//wHVlZW+qiFiIgMoNog+Oc//4kJEyZg1apVkEgklZ7/9NNPdVoYERHp\nR7VB0LZtWwCAi4uL3oohIiL9qzYI/Pz8AADOzs7o3Lmz3goiIiL90ngdwZIlSxAYGIgvvvgCV69e\n1UdN9V5aWhpiY2ORlpZm6FKIiF6axoPFX331Fe7evYsjR44gPj4eJSUlCAwMxIcffqiP+uqlbdu2\nISsrCzKZDF5eXoYuh4jopU/XJ+0AAA1USURBVGh18/rmzZtjzJgxmDdvHjp27Ij169fruq56TSaT\nqf0kImrINO4R/PHHHzh8+DCOHj0KGxsbBAYGIi4uTh+1ERGRHmgMgtmzZyMoKAibN2+GVCrVR01E\nRKRHNQaBUqmEg4MDxo4dq696iIhIz2o8RmBsbIzbt2+jrKxMX/UQEZGeaewacnBwwLvvvgs/Pz+1\ncYbGjRun08KIiEg/NAbBm2++iTfffBOCIKCkpEQfNRERkR5pDIKoqCh91EFERAaiMQhGjx5d5aBz\n27dv19h4amoqFi5ciIqKCkRERGDixIlVzvfdd98hOjoa+/fvR5cudX/zBiIiQ7AwFtR+1lcag2Dm\nzJmq/8vlchw9ehTGxsYaG1YqlUhISMDWrVshlUoxdOhQ+Pn5oV27dmrzPX78GNu3b4erq+sLlE9E\nVH8NaVWCb3MbYYBj/b74VGMQ/HX00e7du2Po0KEaG87IyICTkxMcHR0BAMHBwTh27FilIFi9ejUm\nTJiALVu21KZuIqJ6z9WuDK529f+sS41B8ODBA9X/KyoqcPnyZRQXF2tsuKCgAC1btlRNS6VSZGRk\nqM1z+fJl5Ofn429/+5vWQSCXyw1+v+Rnp9OWlZXVeS2dOnWq0/b0xdDbpD7gtmvYxLz9NAZBWFgY\nJBIJBEGAiYkJHBwcsHDhwpdecEVFBZYsWYLFixfX6nXm5uYG32BmZmaqn4aupb7g+9Bwcds1bNpu\nv5oCQ2MQHD9+XPuKniOVSpGfn6+aLigoUBuioqSkBFevXsWYMWMAAHfv3sWUKVOwYcMGHjAmItKj\naoMgIyMD9vb2aN68OQDg4MGD+O677/DGG28gKioKNjY2NTbcpUsXZGdnIzc3F1KpFCkpKVixYoXq\neWtra5w7d041PXr0aMyYMYMhQESkZ9UOMfHZZ5/B1NQUAPDTTz9h+fLlGDx4MKysrBAfH6+xYRMT\nE8THx2P8+PEICgpCYGAg2rdvj9WrV+PYsWN1twZERPRSqt0jUCqVqm/9hw8fxrBhwxAQEICAgACE\nhoZq1bivry98fX3VHouJialy3q+++krbmomIqA5Vu0dQUVGB8vJyAMDZs2fV7sSlVCp1X1kdkCsa\nRp1ERIZU7R5BcHAwRo0ahWbNmsHCwgIeHh4AgBs3bsDKykpvBb4Mc1NjdJ+u+Qro2rK+VwxjADn3\niuu8/Z+XjanT9oiINKk2CKZMmYJevXrh7t276N27t2qYiYqKCsyZM0dvBRIRkW7VePqom5tbpcda\nt26ts2KIiEj/tLp5PRERvboYBEREIscgICISOQYBEZHIMQiIiESOQUBEJHIMAiIikWMQEBGJHIOA\niEjkGARERCLHICAiEjkGARGRyDEIiIhEjkHwAgQjE7WfREQNGYPgBTx53R0Kq5Z48rq7oUshInpp\n/Er7AsqbOqC8qYOhyyAiqhPcIyAiEjkGARGRyDEIiIhEjkFARCRyDAIiIpFjEBARiRyDgIhI5BgE\nREQixyAgUUlLS0NsbCzS0tIMXQpRvcEri0lUtm3bhqysLMhkMnh5eRm6HKJ6gXsEJCoymUztJxEx\nCIiIRE+nQZCamoqAgAD4+/tj06ZNlZ7funUrgoKCEBISgrFjx+LmzZu6LIeIiKqgsyBQKpVISEjA\n5s2bkZKSguTkZFy7dk1tnk6dOuHAgQNISkpCQEAAli1bpqtyiIioGjoLgoyMDDg5OcHR0RFmZmYI\nDg7GsWPH1Obx8vKCpaUlAMDNzQ35+fm6KoeIiKqhs7OGCgoK0LJlS9W0VCpFRkZGtfPv378fb7/9\ntsZ25XI5MjMztaqhU6dOWs1HL0/bbWJoZWVlqp91XXND/bw1lG2na2LefvXi9NFDhw7h119/xY4d\nOzTOa25u3mA32KusoWwTMzMz1c+GUrOu8X1o2LTdfjUFhs6CQCqVqnX1FBQUQCqVVprvzJkz2Lhx\nI3bs2KH6JSUiIv3R2TGCLl26IDs7G7m5uSgrK0NKSgr8/PzU5vntt98QHx+PDRs2wM7OTlelEBFR\nDXS2R2BiYoL4+HiMHz8eSqUS4eHhaN++PVavXg0XFxf07dsXS5cuhUwmQ0xMDADA3t4eGzdu1FVJ\nRERUBZ0eI/D19YWvr6/aY8/+6ANPL/cnIiLD4pXFRNRgcNBA3agXZw0REWmDgwbqBvcIiKjB4KCB\nusEgoHpLrlAaugQiUWDXENVb5qbG6D59e522aX2vGMYAcu4V13nbPy8bU6ftEekL9wiIiESOQUBE\nJHIMAiIikWMQEFGd44H+hoUHi4mozuniQD/Ag/26wj0CIiKRYxAQEYkcg4CISOQYBEREIscgICIS\nOQYBETUYgpGJ2k+qGwwCImownrzuDoVVSzx53d3QpbxSGKskKvxG2bCVN3VAeVMHQ5fxyuEeAYkK\nv1ESVcavRSQq/EZJVBn3CIiIRI5BQEQkcgwCIiKRYxAQEYkcg4CISOQYBEREIscgICISOQYBEZHI\nMQiIiESOQUBEJHIMAiIikWMQEBGJHIOAiEjkdBoEqampCAgIgL+/PzZt2lTp+bKyMnz00Ufw9/dH\nREQE8vLydFkOERFVQWdBoFQqkZCQgM2bNyMlJQXJycm4du2a2jxff/01mjRpgv/+97947733sHz5\ncl2VQ0RE1dBZEGRkZMDJyQmOjo4wMzNDcHAwjh07pjbP8ePHMWTIEABAQEAAzp49C0EQdFUSERFV\nQWc3pikoKEDLli1V01KpFBkZGZXmsbe3f1qIiQmsra1RVFQEW1vbatuVy+XIzMzUuo4d7/eoZeWG\nlZmZCUTsM3QZtVKb7VFbDWn7cdupa0jbDnj1t59cLq/2uQZ3hzI3NzdDl0BE9ErRWdeQVCpFfn6+\narqgoABSqbTSPLdv3wYAlJeXo7i4GM2aNdNVSUREVAWdBUGXLl2QnZ2N3NxclJWVISUlBX5+fmrz\n+Pn54ZtvvgEAfPfdd/Dy8oJEItFVSUREVAWJoMOjs6dOncKiRYugVCoRHh6OKVOmYPXq1XBxcUHf\nvn0hl8sxffp0ZGZmomnTpli1ahUcHR11VQ4REVVBp0FARET1H68sJiISOQYBEZHINbjTR19Vs2bN\nwsmTJ2FnZ4fk5GRDl0O1cPv2bcyYMQP379+HRCJBZGQkxo4da+iySEtyuRwjR45EWVkZlEolAgIC\nEB0dbeiy9IrHCOqJn376CY0aNcLMmTMZBA3MnTt3cPfuXXTu3BmPHz9GeHg41q1bh3bt2hm6NNKC\nIAiQyWRo3LgxFAoFRowYgU8++URU1yyxa6ie6NGjB5o2bWroMugFtGjRAp07dwYAWFlZoU2bNigo\nKDBwVaQtiUSCxo0bA3h6PVN5ebnoTmNnEBDVoby8PGRmZsLV1dXQpVAtKJVKhIaGwtvbG97e3qLb\nfgwCojpSUlKC6OhozJ49G1ZWVoYuh2rB2NgYhw4dwqlTp5CRkYGrV68auiS9YhAQ1QGFQoHo6GiE\nhISgf//+hi6HXlCTJk3g6emJ06dPG7oUvWIQEL0kQRDwySefoE2bNhg3bpyhy6FaKiwsxKNHjwAA\nT548wZkzZ9CmTRsDV6VfPGuonoiNjcX58+dRVFQEOzs7TJ06FREREYYui7SQnp6OkSNHwtnZGUZG\nT79bxcbGwtfX18CVkTauXLmCuLg4KJVKCIKAAQMGICoqytBl6RWDgIhI5Ng1REQkcgwCIiKRYxAQ\nEYkcg4CISOQYBEREIsfRR4n+olOnTnB2doZSqUSbNm3w+eefw9LSssp5165di0aNGuGDDz7Qc5VE\ndYd7BER/YWFhgUOHDiE5ORmmpqbYs2ePoUsi0inuERDVwMPDA7///jsA4ODBg9iyZQskEgk6dOiA\nZcuWqc27b98+7N27FwqFAk5OTli6dCksLS1x5MgRrFu3DkZGRrC2tsbOnTuRlZWFWbNmQaFQoKKi\nAmvXrkWrVq0MsIZEDAKiapWXlyM1NRV9+vRBVlYWNmzYgN27d8PW1hYPHjyoNL+/vz8iIyMBAKtW\nrcL+/fsxevRorF+/Hlu2bIFUKlUNZbBnzx6MGTMGgwYNQllZGSoqKvS6bkTPYxAQ/cWTJ08QGhoK\n4OkewdChQ7F3714MGDAAtra2AAAbG5tKr8vKysIXX3yB4uJilJSUwMfHBwDg7u6OuLg4BAYGwt/f\nHwDg5uaGjRs3Ij8/H/379+feABkUg4DoL54dI6ituLg4rF+/Hh07dkRiYiLOnz8PAEhISMClS5dw\n8uRJhIeH48CBAwgJCYGrqytOnjyJiRMnYt68eejVq1ddrwqRVniwmEgLXl5e+Pbbb1FUVAQAVXYN\nlZSUoHnz5lAoFEhKSlI9npOTA1dXV8TExKBZs2bIz89Hbm4uHB0dMWbMGPTt21d1HILIELhHQKSF\n9u3bY/LkyRg9ejSMjIzw1ltvYcmSJWrzxMTEICIiAra2tnB1dUVJSQkAYOnSpbhx4wYEQYCXlxc6\nduyIf/7znzh06BBMTEzw2muvYdKkSYZYLSIAHH2UiEj02DVERCRyDAIiIpFjEBARiRyDgIhI5BgE\nREQixyAgIhI5BgERkcj9Pxr3buwlybcKAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "ac54f705c3fc462445cad2de978d7077646f933a",
        "id": "RW58fAfw6dwf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "c7fb8751-90cc-4362-ced1-ff51bae640c2"
      },
      "source": [
        "sns.barplot(x=\"Sex\", y=\"Survived\", hue=\"Pclass\", data=training)\n",
        "plt.ylabel(\"Survival Rate\")\n",
        "plt.title(\"Survival Rates Based on Gender and Class\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Survival Rates Based on Gender and Class')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cDE9PTyxatAirVq0qt88ZeLr77+npCU9PT/j7++Phw4dYvnw5AKB///4YMGAAhg0bBn9/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\niIgk7v8BxI/nXZEhz0QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "13dde8426cb8a654e6f50cb1855e4369c362ea68",
        "id": "lUzsF0Wj6dwg",
        "colab_type": "text"
      },
      "source": [
        "It appears that class also plays a role in survival, as shown by the bar graph. People in Pclass 1 were more likely to survive than people in the other 2 Pclasses."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "ad10453d5d42100bfb65be57900ff1554508a95c",
        "id": "JZRG2n4n6dwh",
        "colab_type": "text"
      },
      "source": [
        "**Age**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "323af5a70d2033f9fa1b5cc8286a675869ba302e",
        "id": "eSj5fiJv6dwh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "a982e82f-bbf9-411d-c381-7b1d41deb9d1"
      },
      "source": [
        "survived_ages = training[training.Survived == 1][\"Age\"]\n",
        "not_survived_ages = training[training.Survived == 0][\"Age\"]\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.distplot(survived_ages, kde=False)\n",
        "plt.axis([0, 100, 0, 100])\n",
        "plt.title(\"Survived\")\n",
        "plt.ylabel(\"Proportion\")\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.distplot(not_survived_ages, kde=False)\n",
        "plt.axis([0, 100, 0, 100])\n",
        "plt.title(\"Didn't Survive\")\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1g6+vL1JTUwEACQkJ8Pf313VpRB3C3iZzofMZh5eXF8aPH4/JkydDKpXCw8MD06ZNw9//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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "46374be19c92dbeb146493226b47701820a1bc7f",
        "id": "pbe8tmMc6dwj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "3a1410ed-dab6-480c-a63f-59fd01f48999"
      },
      "source": [
        "sns.stripplot(x=\"Survived\", y=\"Age\", data=training, jitter=True)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9507b4cf98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sxnaHQbcJcbcDXze1D74D9n/ddI3K3Xa+gKQRafidgCqdgb1nLVc1nm5KFt87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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "56bcea2157a75ed314388f1804aa2271cc631f25",
        "id": "mqM3PIRG6dwl",
        "colab_type": "text"
      },
      "source": [
        "It appears as though passengers in the younger range of ages were more likely to survive than those in the older range of ages, as seen by the clustering in the strip plot, as well as the survival distributions of the histogram."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "60d068f1155860f57f3395bd8bc6b752c050c8e5",
        "id": "NalZK_Za6dwl",
        "colab_type": "text"
      },
      "source": [
        "Here is one final cumulative graph of a pair plot that shows the relations between all of the different features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "7d98aac15ffac0c0a3282961850dabc42ae79b1c",
        "id": "JmqTI1DX6dwm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "86f51a46-b5cd-4071-a828-26fac58f3508"
      },
      "source": [
        "sns.pairplot(training)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f9507d17cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fbXvx8NWjMKB//25qX89g175DDS32ofBKcqyLYvXdpoB9vwUSmycONbSg/EA9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\noNEuSwmgMAZV10BcmXPdBhUd73xah1mj+4MxBkII/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m5yGBJN+gpymUFo6UvLulhSOh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FTCI16fvypMDOi0JH41ZShyrCZYkxjkWE341ZSh0Gl79GdWBMfJx24HjKBUqFDR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sXiCURC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\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t2Gses33NMsyf3mgOoItttxt/9vHZMRd7rdD7WKW5bfUSc1BfbjkfGojh2KlETvkWy2u5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\nQgghhBBlRDbfhBBCCCGEEEIIIYQoI/8DRtDdQfFV+qgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1260x1260 with 56 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "5a208dd95d6b8546f25d02a55822fb84800e3772",
        "id": "iE297Fp66dwp",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"p5\"></a>\n",
        "# 5. Feature Engineering\n",
        "Because values in the Sex and Embarked columns are categorical values, we have to represent these strings as numerical values in order to perform our classification with our model. We can also do this process through **One-Hot-Encoding**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "bbf26333a83e030e896203276d0d361ec021ac35",
        "scrolled": false,
        "id": "o06fQ6C06dwr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "11ae3b52-0a68-4cc3-a2e9-66d1bd5bfbe4"
      },
      "source": [
        "training.sample(5)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
              "      <td>female</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>80.0000</td>\n",
              "      <td>S</td>\n",
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              "      <th>85</th>\n",
              "      <td>86</td>\n",
              "      <td>1</td>\n",
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              "      <td>Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...</td>\n",
              "      <td>female</td>\n",
              "      <td>33.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>15.8500</td>\n",
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              "      <th>107</th>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Moss, Mr. Albert Johan</td>\n",
              "      <td>male</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7750</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>702</th>\n",
              "      <td>703</td>\n",
              "      <td>0</td>\n",
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              "      <td>Barbara, Miss. Saiide</td>\n",
              "      <td>female</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>14.4542</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Parch     Fare  Embarked\n",
              "18            19         0       3  ...     0  18.0000         S\n",
              "829          830         1       1  ...     0  80.0000         S\n",
              "85            86         1       3  ...     0  15.8500         S\n",
              "107          108         1       3  ...     0   7.7750         S\n",
              "702          703         0       3  ...     1  14.4542         C\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "66934ee8a0f64f0faa2afce91d9e28a4a1b56864",
        "id": "vUiw4V7K6dwt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "719403a4-14c8-430a-ad9e-1c24ed8797f4"
      },
      "source": [
        "testing.sample(5)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>1080</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Miss. Ada</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>69.550</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>345</th>\n",
              "      <td>1237</td>\n",
              "      <td>3</td>\n",
              "      <td>Abelseth, Miss. Karen Marie</td>\n",
              "      <td>female</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.650</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>274</th>\n",
              "      <td>1166</td>\n",
              "      <td>3</td>\n",
              "      <td>Saade, Mr. Jean Nassr</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.225</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>917</td>\n",
              "      <td>3</td>\n",
              "      <td>Robins, Mr. Alexander A</td>\n",
              "      <td>male</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>14.500</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>964</td>\n",
              "      <td>3</td>\n",
              "      <td>Nieminen, Miss. Manta Josefina</td>\n",
              "      <td>female</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.925</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Pclass  ...    Fare Embarked\n",
              "188         1080       3  ...  69.550        S\n",
              "345         1237       3  ...   7.650        S\n",
              "274         1166       3  ...   7.225        C\n",
              "25           917       3  ...  14.500        S\n",
              "72           964       3  ...   7.925        S\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "adbfc2d35676144cea7a3a80775229d51030990d",
        "id": "EueozJK76dwu",
        "colab_type": "text"
      },
      "source": [
        "We change Sex to binary, as either 1 for female or 0 for male. We do the same for Embarked. We do this same process on both the training and testing set to prepare our data for Machine Learning."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "18893231d1a1ec187e76095762e9f278d91e0714",
        "id": "wPn7ewc-6dwv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "training.loc[training[\"Sex\"] == \"male\", \"Sex\"] = 0\n",
        "training.loc[training[\"Sex\"] == \"female\", \"Sex\"] = 1\n",
        "\n",
        "training.loc[training[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n",
        "training.loc[training[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n",
        "training.loc[training[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n",
        "\n",
        "testing.loc[testing[\"Sex\"] == \"male\", \"Sex\"] = 0\n",
        "testing.loc[testing[\"Sex\"] == \"female\", \"Sex\"] = 1\n",
        "\n",
        "testing.loc[testing[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n",
        "testing.loc[testing[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n",
        "testing.loc[testing[\"Embarked\"] == \"Q\", \"Embarked\"] = 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "b857a8e6e2e9542d53c01e4699f8d101780d5fee",
        "id": "uE6fRYq06dww",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "041e9892-2a48-4f40-9031-5513e7a4fcfa"
      },
      "source": [
        "testing.sample(10)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
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              "      <th>199</th>\n",
              "      <td>1091</td>\n",
              "      <td>3</td>\n",
              "      <td>Rasmussen, Mrs. (Lena Jacobsen Solvang)</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>950</td>\n",
              "      <td>3</td>\n",
              "      <td>Davison, Mr. Thomas Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>16.1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>386</th>\n",
              "      <td>1278</td>\n",
              "      <td>3</td>\n",
              "      <td>Aronsson, Mr. Ernst Axel Algot</td>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7750</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>3</td>\n",
              "      <td>Saether, Mr. Simon Sivertsen</td>\n",
              "      <td>0</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>1014</td>\n",
              "      <td>1</td>\n",
              "      <td>Schabert, Mrs. Paul (Emma Mock)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>57.7500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>922</td>\n",
              "      <td>2</td>\n",
              "      <td>Louch, Mr. Charles Alexander</td>\n",
              "      <td>0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>1128</td>\n",
              "      <td>1</td>\n",
              "      <td>Warren, Mr. Frank Manley</td>\n",
              "      <td>0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>75.2500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>899</td>\n",
              "      <td>2</td>\n",
              "      <td>Caldwell, Mr. Albert Francis</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>29.0000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>1078</td>\n",
              "      <td>2</td>\n",
              "      <td>Phillips, Miss. Alice Frances Louisa</td>\n",
              "      <td>1</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>21.0000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>288</th>\n",
              "      <td>1180</td>\n",
              "      <td>3</td>\n",
              "      <td>Mardirosian, Mr. Sarkis</td>\n",
              "      <td>0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2292</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Pclass  ...     Fare  Embarked\n",
              "199         1091       3  ...   8.1125         0\n",
              "58           950       3  ...  16.1000         0\n",
              "386         1278       3  ...   7.7750         0\n",
              "415         1307       3  ...   7.2500         0\n",
              "122         1014       1  ...  57.7500         1\n",
              "30           922       2  ...  26.0000         0\n",
              "236         1128       1  ...  75.2500         1\n",
              "7            899       2  ...  29.0000         0\n",
              "186         1078       2  ...  21.0000         0\n",
              "288         1180       3  ...   7.2292         1\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "49a1c9d969495eacc54b5e03086ee4e682f7c472",
        "id": "h_srwh0u6dwy",
        "colab_type": "text"
      },
      "source": [
        "We can combine SibSp and Parch into one synthetic feature called family size, which indicates the total number of family members on board for each member. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "889c1c9d5c7a42f56562f12dfe9fc76cd4949cd1",
        "id": "6LIn6Ztd6dwy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "training[\"FamSize\"] = training[\"SibSp\"] + training[\"Parch\"] + 1\n",
        "testing[\"FamSize\"] = testing[\"SibSp\"] + testing[\"Parch\"] + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "f41eb8b24fa207451ef641776189afb16c811c5d",
        "id": "dRWGPIhf6dwz",
        "colab_type": "text"
      },
      "source": [
        "This IsAlone feature also may work well with the data we're dealing with, telling us whether the passenger was along or not on the ship."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d42df43ea7691877bb3875d15ce2804962f1e70e",
        "id": "UwbnHbpk6dw1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "training[\"IsAlone\"] = training.FamSize.apply(lambda x: 1 if x == 1 else 0)\n",
        "testing[\"IsAlone\"] = testing.FamSize.apply(lambda x: 1 if x == 1 else 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "e57b637bc0e7028f1373559b8b8485fb983fa456",
        "id": "MufXl5Yq6dw2",
        "colab_type": "text"
      },
      "source": [
        "Although it may not seem like it, we can also extract some useful information from the name column. Not the actual names themselves, but the title of their names like Ms. or Mr. This may also provide a hint as to whether the passenger survived or not. Therefore we can extract this title and then encode it like we did for Sex and Embarked."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "fb584d57bfc2e25e1e106aeb117c87ee9ffa3c75",
        "id": "yUgV5hf06dw3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for name in training[\"Name\"]:\n",
        "    training[\"Title\"] = training[\"Name\"].str.extract(\"([A-Za-z]+)\\.\",expand=True)\n",
        "    \n",
        "for name in testing[\"Name\"]:\n",
        "    testing[\"Title\"] = testing[\"Name\"].str.extract(\"([A-Za-z]+)\\.\",expand=True)\n",
        "    \n",
        "title_replacements = {\"Mlle\": \"Other\", \"Major\": \"Other\", \"Col\": \"Other\", \"Sir\": \"Other\", \"Don\": \"Other\", \"Mme\": \"Other\",\n",
        "          \"Jonkheer\": \"Other\", \"Lady\": \"Other\", \"Capt\": \"Other\", \"Countess\": \"Other\", \"Ms\": \"Other\", \"Dona\": \"Other\", \"Rev\": \"Other\", \"Dr\": \"Other\"}\n",
        "\n",
        "training.replace({\"Title\": title_replacements}, inplace=True)\n",
        "testing.replace({\"Title\": title_replacements}, inplace=True)\n",
        "\n",
        "training.loc[training[\"Title\"] == \"Miss\", \"Title\"] = 0\n",
        "training.loc[training[\"Title\"] == \"Mr\", \"Title\"] = 1\n",
        "training.loc[training[\"Title\"] == \"Mrs\", \"Title\"] = 2\n",
        "training.loc[training[\"Title\"] == \"Master\", \"Title\"] = 3\n",
        "training.loc[training[\"Title\"] == \"Other\", \"Title\"] = 4\n",
        "\n",
        "testing.loc[testing[\"Title\"] == \"Miss\", \"Title\"] = 0\n",
        "testing.loc[testing[\"Title\"] == \"Mr\", \"Title\"] = 1\n",
        "testing.loc[testing[\"Title\"] == \"Mrs\", \"Title\"] = 2\n",
        "testing.loc[testing[\"Title\"] == \"Master\", \"Title\"] = 3\n",
        "testing.loc[testing[\"Title\"] == \"Other\", \"Title\"] = 4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "4c82b45238c0405bfa93f028af4f96d0c8b6f760",
        "scrolled": true,
        "id": "vL92UcBY6dw4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "60facb2a-a855-4a77-ef4d-4e8dc0d63c96"
      },
      "source": [
        "print(set(training[\"Title\"]))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{0, 1, 2, 3, 4}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "382e3a9d9a34fe26c3f0494ff5d3fd48df59038b",
        "id": "AzL1XFbH6dw6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "024277d1-1ab2-4b14-9a96-5d9d1757e7ee"
      },
      "source": [
        "training.sample(5)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>FamSize</th>\n",
              "      <th>IsAlone</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>213</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Perkin, Mr. John Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>638</th>\n",
              "      <td>639</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Panula, Mrs. Juha (Maria Emilia Ojala)</td>\n",
              "      <td>1</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>39.6875</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>167</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Chibnall, Mrs. (Edith Martha Bowerman)</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>55.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>280</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Abbott, Mrs. Stanton (Rosa Hunt)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>611</th>\n",
              "      <td>612</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Jardin, Mr. Jose Neto</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... FamSize  IsAlone  Title\n",
              "212          213         0       3  ...       1        1      1\n",
              "638          639         0       3  ...       6        0      2\n",
              "166          167         1       1  ...       2        0      2\n",
              "279          280         1       3  ...       3        0      2\n",
              "611          612         0       3  ...       1        1      1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "41de656234ed079e07f5390372c3a9306a17e98a",
        "id": "uNEaYNcX6dw7",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"p6\"></a>\n",
        "# 6. Model Fitting and Predicting\n",
        "Now that our data has been processed and formmated properly, and that we understand the general data we're working with as well as the trends and associations, we can start to build our model. We can import different classifiers from sklearn. We will try different types of models to see which one gives the best accuracy for its predictions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "2c7048c1e2d22005039a58fea15ebadcc27ec2bf",
        "id": "9bVSi1yf6dw8",
        "colab_type": "text"
      },
      "source": [
        "**sklearn Models to Test**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "247cf16dde7c8b0403beae7a93322bf2863ec6f9",
        "id": "HwAMNwum6dw8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "2cd333e04846dcd31b6a622fe901d4f55c958bd6",
        "id": "SEBScPSc6dw9",
        "colab_type": "text"
      },
      "source": [
        "To evaluate our model performance, we can use the make_scorere and accuracy_score function from sklearn metrics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "63e0f0169267edd3e4c45ae829476f51e4d1df0e",
        "id": "JrL6Vcjw6dw-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import make_scorer, accuracy_score "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "92194bb407126ddec5273bb45fdbd5d515854e0a",
        "id": "zw_g0xd56dxA",
        "colab_type": "text"
      },
      "source": [
        "We can also use a GridSearch cross validation to find the optimal parameters for the model we choose to work with and use to predict on our testing set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "9bd1078ce44840c71b2e4fe6a0651b538d892995",
        "id": "JN4__Vsu6dxA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "db3842b4fcfcf0d1e7b0c802217b7a38d489ccb8",
        "id": "0M86PgzQ6dxB",
        "colab_type": "text"
      },
      "source": [
        "**Defining Features in Training/Test Set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d9484f14efc825d2eb1e5496995d57d6a788de22",
        "id": "ug3U_ZU36dxC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = [\"Pclass\", \"Sex\", \"Age\", \"Embarked\", \"Fare\", \"FamSize\", \"IsAlone\", \"Title\"]\n",
        "X_train = training[features] #define training features set\n",
        "y_train = training[\"Survived\"] #define training label set\n",
        "X_test = testing[features] #define testing features set\n",
        "#we don't have y_test, that is what we're trying to predict with our model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "a008d1b7a4a90e34b2844922295f8146823f02ee",
        "id": "gTnwCD5Z6dxD",
        "colab_type": "text"
      },
      "source": [
        "**Validation Data Set**\n",
        "\n",
        "Although we already have a test set, it is generally easy to overfit the data with these classifiers. It is therefore useful to have a third data set called the validation data set to ensure that our model doesn't overfit with the data. We can make this third data set with sklearn's train_test_split function. We can also use the validation data set to test the general accuracy of our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "525f4a8f4119d03e20726b7b594d037e3fa30172",
        "id": "Daz_sPK46dxE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split #to create validation data set\n",
        "\n",
        "X_training, X_valid, y_training, y_valid = train_test_split(X_train, y_train, test_size=0.2, random_state=0) #X_valid and y_valid are the validation sets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "d3597926b012f917a34e9c2369baaa35b4e79452",
        "id": "KbGa0BJj6dxF",
        "colab_type": "text"
      },
      "source": [
        "**SVC Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "39fd28f225d9f1a9d283aa441e5e57508cbc7474",
        "id": "HjYynI4-6dxF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "bf2f6435-d840-42f5-b15b-a005b4bafa2b"
      },
      "source": [
        "svc_clf = SVC() \n",
        "svc_clf.fit(X_training, y_training)\n",
        "pred_svc = svc_clf.predict(X_valid)\n",
        "acc_svc = accuracy_score(y_valid, pred_svc)\n",
        "\n",
        "print(acc_svc)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7262569832402235\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "e6fa08797335fb70e5399aac9d0aa418ddc91416",
        "id": "F0GYRGz56dxH",
        "colab_type": "text"
      },
      "source": [
        "**LinearSVC Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "58b88de30c4a4fa7273cf63e50a54a71fb70e83b",
        "id": "HcKgxBag6dxH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "279ee4b6-9679-4dc4-b0ea-bc405c305441"
      },
      "source": [
        "linsvc_clf = LinearSVC()\n",
        "linsvc_clf.fit(X_training, y_training)\n",
        "pred_linsvc = linsvc_clf.predict(X_valid)\n",
        "acc_linsvc = accuracy_score(y_valid, pred_linsvc)\n",
        "\n",
        "print(acc_linsvc)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7821229050279329\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "e2a0d34546d72e87ea6b6cc8515040fd3f315227",
        "id": "Gvw5Xz2E6dxI",
        "colab_type": "text"
      },
      "source": [
        "**RandomForest Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "1a64bcaa1f6beb3fb70f1fe0c8565379a4cce677",
        "id": "I5fHSiNL6dxJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "b23b6454-a6a1-40d4-b3d3-c4928958c5de"
      },
      "source": [
        "rf_clf = RandomForestClassifier()\n",
        "rf_clf.fit(X_training, y_training)\n",
        "pred_rf = rf_clf.predict(X_valid)\n",
        "acc_rf = accuracy_score(y_valid, pred_rf)\n",
        "\n",
        "print(acc_rf)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8212290502793296\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "be2afad3f04bb3317225d689198ce2f817d1634d",
        "id": "SmDHSADr6dxK",
        "colab_type": "text"
      },
      "source": [
        "**LogisiticRegression Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "1fb3aef22962da43e521b386c754c9b2c7ba0e9e",
        "id": "4_Pm02zL6dxL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "43e18304-d7b6-4196-8eaa-16ed4feff1af"
      },
      "source": [
        "logreg_clf = LogisticRegression()\n",
        "logreg_clf.fit(X_training, y_training)\n",
        "pred_logreg = logreg_clf.predict(X_valid)\n",
        "acc_logreg = accuracy_score(y_valid, pred_logreg)\n",
        "\n",
        "print(acc_logreg)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7988826815642458\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "b171f05d72bdc35ce4bb6c64ac22acb06986c4ed",
        "id": "mVA9tSiz6dxM",
        "colab_type": "text"
      },
      "source": [
        "**KNeighbors Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "4db5787d77d70de18781b2b15cd5ad5b16d0e2b0",
        "id": "i46vkYGA6dxO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "979dd8ad-1c89-4fe8-aece-05260589c1f9"
      },
      "source": [
        "knn_clf = KNeighborsClassifier()\n",
        "knn_clf.fit(X_training, y_training)\n",
        "pred_knn = knn_clf.predict(X_valid)\n",
        "acc_knn = accuracy_score(y_valid, pred_knn)\n",
        "\n",
        "print(acc_knn)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7430167597765364\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "be52b3ffcc1f25f5c6bc03ff8a94802f6693657b",
        "id": "ih2Oo7Bu6dxP",
        "colab_type": "text"
      },
      "source": [
        "**GaussianNB Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "7c8531ec4857b0fb7ccdfc153fbd7a6ca07e4fef",
        "id": "Pj-0nNaM6dxQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8a7f690f-8c9a-4b16-b578-adaf81ec9e0e"
      },
      "source": [
        "gnb_clf = GaussianNB()\n",
        "gnb_clf.fit(X_training, y_training)\n",
        "pred_gnb = gnb_clf.predict(X_valid)\n",
        "acc_gnb = accuracy_score(y_valid, pred_gnb)\n",
        "\n",
        "print(acc_gnb)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7877094972067039\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "0160729b9da414de164ca5aec33d70302014f7ea",
        "id": "8kbnbtu66dxR",
        "colab_type": "text"
      },
      "source": [
        "**DecisionTree Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "5b66cede005b80b15db98dadfe25453f79cff61c",
        "id": "nAcvwcsS6dxR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "00a9d9e3-7ec3-4257-b9f3-c146cbde8e58"
      },
      "source": [
        "dt_clf = DecisionTreeClassifier()\n",
        "dt_clf.fit(X_training, y_training)\n",
        "pred_dt = dt_clf.predict(X_valid)\n",
        "acc_dt = accuracy_score(y_valid, pred_dt)\n",
        "\n",
        "print(acc_dt)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.770949720670391\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "4a010eb3bd4a277eb2aa21fb5a905b9f7e9a74a9",
        "id": "7QmfOGv-6dxT",
        "colab_type": "text"
      },
      "source": [
        "**XGBoost Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "986b1e492627eb16ab64ca42737be67bc401a8d2",
        "id": "bSoiSuEo6dxT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1d0c05c7-2bc5-4092-e411-10369214c71d"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "xg_clf = XGBClassifier(objective=\"binary:logistic\", n_estimators=10, seed=123)\n",
        "xg_clf.fit(X_training, y_training)\n",
        "pred_xg = xg_clf.predict(X_valid)\n",
        "acc_xg = accuracy_score(y_valid, pred_xg)\n",
        "\n",
        "print(acc_xg)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7988826815642458\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "adcda2b37a78d95205f14fbe4c580d4e0174088b",
        "id": "grfXEA6a6dxW",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"p7\"></a>\n",
        "# 7. Evaluating Model Performances\n",
        "After making so many models and predictions, we should evaluate and see which model performed the best and which model to use on our testing set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "032b969ac47e3ea4fb7b77a0d43411f5184012ca",
        "id": "yy6vCiqA6dxX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "41e64add-f39e-421c-d065-39bae0406231"
      },
      "source": [
        "model_performance = pd.DataFrame({\n",
        "    \"Model\": [\"SVC\", \"Linear SVC\", \"Random Forest\", \n",
        "              \"Logistic Regression\", \"K Nearest Neighbors\", \"Gaussian Naive Bayes\",  \n",
        "              \"Decision Tree\", \"XGBClassifier\"],\n",
        "    \"Accuracy\": [acc_svc, acc_linsvc, acc_rf, \n",
        "              acc_logreg, acc_knn, acc_gnb, acc_dt, acc_xg]\n",
        "})\n",
        "\n",
        "model_performance.sort_values(by=\"Accuracy\", ascending=False)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.821229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.798883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>XGBClassifier</td>\n",
              "      <td>0.798883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gaussian Naive Bayes</td>\n",
              "      <td>0.787709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Linear SVC</td>\n",
              "      <td>0.782123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.770950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>K Nearest Neighbors</td>\n",
              "      <td>0.743017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SVC</td>\n",
              "      <td>0.726257</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Model  Accuracy\n",
              "2         Random Forest  0.821229\n",
              "3   Logistic Regression  0.798883\n",
              "7         XGBClassifier  0.798883\n",
              "5  Gaussian Naive Bayes  0.787709\n",
              "1            Linear SVC  0.782123\n",
              "6         Decision Tree  0.770950\n",
              "4   K Nearest Neighbors  0.743017\n",
              "0                   SVC  0.726257"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "2155e211b1bcc8165f32b444a9122316d9d69e49",
        "id": "TY5LuZF56dxZ",
        "colab_type": "text"
      },
      "source": [
        "It appears that the Random Forest model works the best with our data so we will use it on the test set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "294ea2437eec87aa86cd6b4793c88aa374cbbcb2",
        "id": "2sGec2C-6dxZ",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"p8\"></a>\n",
        "# 8. Tuning Parameters with GridSearchCV"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "6e8feefac91cef0a68a90d6c99a498c8e7c0c220",
        "id": "_TumBm1_6dxb",
        "colab_type": "text"
      },
      "source": [
        "We can improve the accuracy of our model by turning the hyperparameters of our Random Forest model. We will run a GridSearchCV to find the best parameters for the model and use that model to train and test our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "cee16030035d751cf9c011b562381e892f71835d",
        "id": "mFn0lelB6dxb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "7aacbc51-49db-4b3b-f7ac-a49b3af7483e"
      },
      "source": [
        "rf_clf = RandomForestClassifier()\n",
        "\n",
        "parameters = {\"n_estimators\": [4, 5, 6, 7, 8, 9, 10, 15], \n",
        "              \"criterion\": [\"gini\", \"entropy\"],\n",
        "              \"max_features\": [\"auto\", \"sqrt\", \"log2\"], \n",
        "              \"max_depth\": [2, 3, 5, 10], \n",
        "              \"min_samples_split\": [2, 3, 5, 10],\n",
        "              \"min_samples_leaf\": [1, 5, 8, 10]\n",
        "             }\n",
        "\n",
        "grid_cv = GridSearchCV(rf_clf, parameters, scoring = make_scorer(accuracy_score))\n",
        "grid_cv = grid_cv.fit(X_train, y_train)\n",
        "\n",
        "print(\"Our optimized Random Forest model is:\")\n",
        "grid_cv.best_estimator_"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Our optimized Random Forest model is:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
              "                       max_depth=10, max_features='sqrt', max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=5, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=6,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "9a59fc2e5923c0f0862a17c8994be909b4e3c8e5",
        "id": "18tIakDZ6dxe",
        "colab_type": "text"
      },
      "source": [
        "Great, now that we have the optimal parameters for our Random Forest model, we can build a new model with those parameters to fit and use on the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d06da79f1940ec20b1e5207649cfd1b36024fb9f",
        "id": "GBn4avpU6dxe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "165323cf-f5aa-4242-cc86-fb5e02fdbe4d"
      },
      "source": [
        "rf_clf = grid_cv.best_estimator_\n",
        "\n",
        "rf_clf.fit(X_train, y_train)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
              "                       max_depth=10, max_features='sqrt', max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=5, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=6,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "eb41e3a1cd4072d991b28899e8c0898410b706ac",
        "id": "ks65Aq1M6dxg",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"p9\"></a>\n",
        "# 9. Submission"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "6d75b5ea02c6830d422e23b6332dfc81ece3bf54",
        "id": "kFihueRI6dxg",
        "colab_type": "text"
      },
      "source": [
        "Let's create a dataframe to submit to the competition with our predictions of our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "3f003a3580bc74e884ca1525421cffbf15f347b0",
        "id": "Vrpkh4_R6dxh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "final_pred =rf_clf.predict(X_test)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "1305526e29c632cec41f6f290382a25fcdd853c2",
        "id": "aCTpLi536dxi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "741e46eb-7d90-455f-95f6-84f3af193fff"
      },
      "source": [
        "Final = pd.DataFrame({\n",
        "        \"PassengerId\": testing[\"PassengerId\"],\n",
        "        \"Survived\": submission_predictions\n",
        "    })\n",
        "\n",
        "Final\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1306</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived\n",
              "0            892         0\n",
              "1            893         0\n",
              "2            894         0\n",
              "3            895         0\n",
              "4            896         1\n",
              "..           ...       ...\n",
              "413         1305         0\n",
              "414         1306         1\n",
              "415         1307         0\n",
              "416         1308         0\n",
              "417         1309         1\n",
              "\n",
              "[418 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "36ee8b4c497778afada1cc969e24011799cdc1ad",
        "id": "6RimOUZk6dxj",
        "colab_type": "text"
      },
      "source": [
        "If you made it this far, congratulations!! You have gotten a glimpse at an introduction to data visualization, analysis and Machine Learning. You are well on your way to become a Data Science expert! Keep learning and trying out new things, as one of the most important things for Data Scientists is to be creative and perform analysis hands-on. Please upvote and share if this kernel helped you!"
      ]
    }
  ]
}